Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Revista Brasileira de Ciência do Solo

Braz Calderano Filho²  Helena Polivanov¹  César Da Silva Chagas²  Waldir De Carvalho Júnior²  Emílio Velloso Barroso¹  Antônio José Teixeira Guerra¹  Sebastião Barreiros Calderano² 

[1] ,Embrapa SolosRio de Janeiro RJ ,Brazil

关键词: artificial neural networks;    terrain attributes;    digital mapping;    redes neurais artificiais;    atributos do terreno;    mapeamento digital;   

DOI  :  10.1590/S0100-06832014000600003

来源: SciELO

【 摘 要 】

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

【 授权许可】

CC BY   
 All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License